Prognostic performance of radiomic data to predict distant metastases and overall survival in patients with early stage (I/II) non-small cell lung cancer (NSCLC)

放射组学数据预测早期 (I/II) 非小细胞肺癌 (NSCLC) 患者远处转移和总生存期的预后表现

• 批准号: 408360143
• 负责人: Dr. Jakob Weiss
• 金额: --
• 依托单位: Dana-Farber / Brigham and Womens Cancer Center
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/408360143?language=en
• 关键词: Prognostic; performance; radiomic; data; predict

Lung cancer is the leading cause of cancer deaths in the world with non-small cell lung cancer (NSCLC) being the most commonly observed subtype. Despite improvements in diagnosis and treatment, prognosis of NSCLC remains poor. Only patients with early-stage disease are treated with curative intent based on surgical resection. However, even after complete and successful resection of the same tumor stage, a wide spectrum of survival periods is observed. In this context, it has become increasingly evident that each tumor is associated with distinct phenotypic characteristics, which are responsible for different treatment responses. This has led to concept of personalized medicine with the aim to tailor treatment strategies towards the individual needs of the patient. Thus, the individual tumor phenotype may serve as a decision-making tool for estimating the individual response to different treatment approaches with the aim to improve overall survival and quality of life. Lung cancer is routinely diagnosed using medical imaging, most commonly computed tomography (CT). Diagnosis is based on the subjective image interpretation of the radiologist according to distinct imaging features with the risk for false diagnosis due to inter-observer variability. Therefore, biopsy of tumor tissue is necessary to establish a final diagnosis. However, biopsy only allows for acquiring small samples of tissue, which may fail to comprehensively capture the molecular variations within tumors, thus posing the risk for underestimating tumor aggressiveness. Therefore, quantitative imaging analysis has gained increasing importance with the aim to non-invasively extract objective image features to describe the tumor phenotype based on mathematical algorithms. This new way of analyzing imaging data has become known as radiomics. There is growing evidence that radiomics may have the potential to serve as a cost-effective and non- invasive biomarker for personalized medicine by providing prognostic information regarding tumor stage, metastatic potential, treatment response and overall survival. Patients with early-stage NSCLC are particularly suitable to evaluate the prognostic impact of radiomics for non-invasive tumor profiling, as CT images and histopathological analysis are routinely available for these patients. Moreover, as mentioned above, despite successful surgical resection of the same disease stage it is well known but not predictable so far, that a certain subpopulation of these patients will experience recurrent disease indicating the need for precision medicine. Therefore, the purpose of the proposed study is to investigate the prognostic performance of radiomic data in patients with early-stage NSCLC with the aim to identify predictive image features to estimate the metastatic potential and overall survival. This is of great importance not only for personalized medicine but also from an interdisciplinary and socioeconomic point of view.

肺癌是世界上癌症死亡的主要原因，其中非小细胞肺癌（NSCLC）是最常见的亚型。尽管诊断和治疗取得了进步，非小细胞肺癌的预后仍然很差。只有早期疾病的患者才会接受基于手术切除的治愈性治疗。然而，即使在同一阶段的肿瘤完全成功切除后，仍观察到广泛的生存期。在这种情况下，越来越明显的是，每种肿瘤都与不同的表型特征相关，这些特征导致不同的治疗反应。这催生了个性化医疗的概念，旨在根据患者的个人需求定制治疗策略。因此，个体肿瘤表型可以作为决策工具来估计个体对不同治疗方法的反应，以提高总体生存率和生活质量。肺癌通常使用医学成像进行诊断，最常见的是计算机断层扫描 (CT)。诊断基于放射科医师根据不同的成像特征进行的主观图像解释，由于观察者之间的差异而存在错误诊断的风险。因此，需要对肿瘤组织进行活检才能做出最终诊断。然而，活检只能获取少量组织样本，可能无法全面捕获肿瘤内的分子变异，从而存在低估肿瘤侵袭性的风险。因此，定量成像分析变得越来越重要，目的是基于数学算法无创地提取客观图像特征来描述肿瘤表型。这种分析成像数据的新方法被称为放射组学。越来越多的证据表明，通过提供有关肿瘤分期、转移潜力、治疗反应和总体生存率的预后信息，放射组学可能有潜力成为个性化医疗的经济高效且非侵入性的生物标志物。早期 NSCLC 患者特别适合评估放射组学对非侵入性肿瘤分析的预后影响，因为这些患者通常可以获得 CT 图像和组织病理学分析。此外，如上所述，尽管成功地手术切除了同一疾病阶段，但众所周知但迄今为止无法预测的是，这些患者的某些亚群将经历疾病复发，这表明需要精准医疗。因此，本研究的目的是调查早期 NSCLC 患者放射组学数据的预后表现，旨在识别预测图像特征以估计转移潜力和总体生存率。这不仅对于个性化医疗非常重要，而且从跨学科和社会经济学的角度来看也非常重要。